Study on the Influence of LED Spectrum on the Visual and Nonvisual Effects in the Interior Zone of Tunnels
Bibliographic record
Abstract
Judicious use of lamps is of profound significance to improve the internal traffic safety of tunnels. This study evaluated the biological effects of spectrum on human visual and nonvisual under mesopic vision category. According to the difference of human eyes’ response to the visual and nonvisual biological effects of different wavelengths of light radiation, a method of applying the mesopic visual spectral luminous efficiency curve to the assessment of nonvisual biological effects and blue light hazards is proposed. The results show that, in theory, under mesopic vision category, the spectrum with high correlation color temperature (CCT) and high color rendering index (CRI) has higher blue light content Z-value and has greater impact on human nonvisual effects and blue light radiation damage, but it can also provide higher human perception luminance. The visual effect detection experiment of driving in the interior zones of the tunnel is simulated in the laboratory, and the critical flicker frequency and reaction time are taken as physiological indexes. The results show that the spectrum with high CCT and high CRI corresponds to lower critical flicker frequency drop value and faster reaction time, which has a positive effect on improving driving safety. Therefore, this paper suggests that the LED with high CCT and high CRI (CCT = 6500 K, CRI2012 = 95) should be used in the interior zones of the tunnel with relatively short lighting section, and the CCT and CRI should be appropriately reduced in other road environments with long lighting section to reduce the impact of spectrum on nonvisual photobiology, so as to improve road traffic safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".